Development of bioinformatics tools for the characterization and classification of low abundant microbes at the strain level, with a study case of SARS-CoV2

Vafadoost, Zahra (2024) Development of bioinformatics tools for the characterization and classification of low abundant microbes at the strain level, with a study case of SARS-CoV2. Masters thesis, Memorail University of Newfoundland.

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Abstract

Microbiome and metagenomics studies are key research areas to understand several diseases important for public health. The latest sequencing technologies have allowed affordable massive sequencing of specimen microenvironment and the characterization of pathological microbe strains. Strain level is the lowest level of taxonomic ranks; the characterization of strain sequences of a pathological microbe helps track new potential virulent variants and vaccine development. My master project aimed to; 1) set a bioinformatics pipeline for specimen metagenomics analysis; and 2) characterize potential bias linked to sequencer technologies. A publicly available dataset of human tissues and SARS-CoV-2 swab specimens from the Global Initiative on Sharing All Influenza Data (GISAID)[1] database was used. In detail, a pipeline was designed to analyze low-abundance metagenomics sequencing data from RNA samples extracted from human tissues. Furthermore, taking advantage of the worldwide effort to track the emergence of SARS-CoV-2 variants, sequencing datasets gathered via the two main sequencing platforms (Illumina and Nanopore) were analyzed to identify potential sequencing biases linked to specific sequencing protocols. Also, a descriptive analysis was generated by applying clustering techniques to the phylogenetic tree and processing and evaluating the metadata's effect on the dataset. Overall, my project guides analyzing metagenomics data for strain characterization when working with low-abundant microbiome data.

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